Publications

Hadamard product in deep learning: Introduction, Advances and Challenges.

Grigorios G Chrysos

Yongtao Wu

Razvan Pascanu

Philip Torr

Volkan Cevher

2025-01-01

IEEE Transactions on Pattern Analysis and Machine Intelligence (published)

doi.org

arxiv.org

Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training

Shahrad Mohammadzadeh

Juan David Guerra

Marco Bonizzato

Reihaneh Rabbany

Golnoosh Farnadi

2025-01-01

ACL (1) (published)

doi.org

arxiv.org

https://www.neuromodec.org/journal/4/2/NzBlvmDpUYspQQbvI4B Online Transcranial Random Noise Stimulation of the Right Temporoparietal Junction Acutely Modulates Human-Machine Social Interactions

Vincent Chamberland

Quentin Moreau

Lisane Moses

Gabriela Milanova

Guillaume Dumas

2025-01-01

Neuromodec Journal (published)

doi.org

ICLR 2025 Workshop on Tackling Climate Change with Machine Learning: Data-Centric Approaches in ML for Climate Action

Konstantin Klemmer

Melissa Chapman

Lily Xu

Poon Kin Ho

Mélisande Teng

Patrick Emami

Yoshua Bengio

Climate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disaste… (see more)rs multiply, sea levels rise, and ecosystems falter. While no silver bullet, machine learning can be an invaluable tool in fighting climate change via a wide array of applications and techniques, from designing smart electric grids to tracking greenhouse gas emissions through satellite imagery. These applications require algorithmic innovations in machine learning and close collaboration with diverse fields and practitioners. This workshop is intended as a forum for those in the global machine learning community who wish to help tackle climate change, and is further aimed to help foster cross-pollination between researchers in machine learning and experts in complementary climate-relevant fields. Building on our past workshops on this topic, this workshop particularly aims to explore data-centric ML approaches for climate action. Data-centric ML is not only a timely topic within the ICLR community, as analyzing and engineering (pre)training datasets becomes increasingly important, but holds specific challenges and opportunities in climate-related areas. We also want to take the opportunity of ICLR being hosted in Singapore to engage with local communities and shine a light on work that deploys, analyzes or critiques ML methods and their use for climate change adaptation and mitigation on the Asian continent.

2025-01-01

ICLR.cc/2025/Workshop_Proposals (published)

openreview.net

An identification of models to help in the design of national strategies and policies to reduce greenhouse gas emissions.

Danielle Maia de Souza

Radhwane Boukelouha

Emma Frejinger

Catherine Morency

Normand Mousseau

Martin Trépanier

2025-01-01

Transportation Research Procedia (published)

doi.org

An identification of models to help in the design of national strategies and policies to reduce greenhouse gas emissions.

Danielle Maia de Souza

Radhwane Boukelouha

Emma Frejinger

Catherine Morency

Normand Mousseau

Martin Trépanier

2025-01-01

Transportation Research Procedia (published)

doi.org

Implicit Diffusion: Efficient Optimization through Stochastic Sampling

Pierre Marion

Anna Korba

Peter Bartlett

Mathieu Blondel

Valentin De Bortoli

Arnaud Doucet

Felipe Llinares-López

Courtney Paquette

Quentin Berthet

2025-01-01

AISTATS (published)

doi.org

openreview.net

Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles

Xing Shen

Hengguan Huang

Brennan Nichyporuk

Tal Arbel

2025-01-01

IEEE Transactions on Medical Imaging (published)

doi.org

arxiv.org

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models

Yinlam Chow

Guy Tennenholtz

Izzeddin Gur

Vincent Zhuang

Bo Dai

Sridhar Thiagarajan

Craig Boutilier

Rishabh Agarwal

Aviral Kumar

Aleksandra Faust

Recent studies have indicated that effectively utilizing inference-time compute is crucial for attaining better performance from large langu… (see more)age models (LLMs). In this work, we propose a novel inference-aware fine-tuning paradigm, in which the model is fine-tuned in a manner that directly optimizes the performance of the inference-time strategy. We study this paradigm using the simple yet effective Best-of-N (BoN) inference strategy, in which a verifier selects the best out of a set of LLM-generated responses. We devise the first imitation learning and reinforcement learning~(RL) methods for BoN-aware fine-tuning, overcoming the challenging, non-differentiable argmax operator within BoN. We empirically demonstrate that our BoN-aware models implicitly learn a meta-strategy that interleaves best responses with more diverse responses that might be better suited to a test-time input -- a process reminiscent of the exploration-exploitation trade-off in RL. Our experiments demonstrate the effectiveness of BoN-aware fine-tuning in terms of improved performance and inference-time compute. In particular, we show that our methods improve the Bo32 performance of Gemma 2B on Hendrycks MATH from 26.8% to 30.8%, and pass@32 from 60.0% to 67.0%, as well as the pass@16 on HumanEval from 61.6% to 67.1%.

2025-01-01

ICLR (published)

doi.org

arxiv.org

Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects

Amir Barda

Matheus Gadelha

Vladimir Kim

Noam Aigerman

Amit H. Bermano

Thibault Groueix

We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without t… (see more)he need for running an SDS type of optimization. Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the bank of available Large Reconstruction Models. We explore different fine-tuning strategies to obtain both multiview generation and inpainting capabilities within the same diffusion model. In particular, the design of the inpainting mask is an important factor of training an inpainting model, and we propose several masking strategies to mimic the types of edits a user would perform on a 3D shape. Our approach takes 3D generative editing from hours to seconds and produces higher-quality results compared to previous works.

2025-01-01

CVPR (published)

doi.org

arxiv.org

Integer Programming Games.

Margarida Carvalho

Gabriele Dragotto

Andrea Lodi

Sriram Sankaranarayanan 0002

2025-01-01

Foundations and Trends® in Optimization (published)

doi.org